README.md

Web App Classifiers Streamlit & Python

Available Machine learning Algorithms

• Support Vector Machine
• Logistic Regression
• Random Forest Classifier
• KNN -On task
• Decision Tree -On task
• Naive Bayes -On task

Available Metrics

• Confusion Matrix
• Accuracy
• Precision
• Recall
• AUC ROC
• ROC Curve
• Precision Recall Curve
• Validation Curve -On task
• Learning Curve -On task

Set up guide

First Get the repository files Downloading or Clonning the repository Now open the folder using any ide or editor and select python. Now navigate to console and type

streamlit run app.py

It will open in localhost server. Now you can use it easily.

Customize Heading and Title of your Task

def main():
    st.title('Binary Classification Web App')
    st.sidebar.title('Binary Classifier App')
    st.markdown("Are Your Mashroom poisonous?")
    st.sidebar.markdown("Are Your Mashroom poisonous?")

    @st.cache(persist = True)
    def load_data():
        df = pd.read_csv('data/mushrooms.csv')
        le = LabelEncoder()

        for col in df.columns:
            df[col] = le.fit_transform(df[col])

        return df

Change the title,sidebar title,markdown and sidebar markdown according to your project.

Environment

Make sure you have all necessery python library installed. For this projects you need

• Numpy
• Pandas
• Streamlit
• Scikit-learn

if you don't have these libraries go to python or anaconda prompt and paste these:

pip install numpy
pip install pandas
pip install streamlit
pip install sklearn

Get touch with Me

Connect- Linkedin
Website- RakibHHridoy